"The only way to achieve the impossible is to believe it is possible." — Charles Kingsleigh
Background[edit | edit source]
Shafquat Rana is a PhD candidate in the Electrical and Computer Engineering Department at the University of Western Ontario, London, Ontario, Canada. She is currently working as a graduate research assistant at Free Appropriate Sustainable (FAST) group lab at University of Western Ontario with focus on Solar Photovoltaics, Heat pumps, Thermal Battery. She has completed her M. Tech in Electrical Engineering at Jamia Millia Islamia, New Delhi, India; with a major in Electrical Power System and Management with a background in Renewable Energy, optimization with a score of 9.6 SGPA. She has completed her B.Tech in Power Engineering with a specialization in Electrical Engineering from National Power Training Institute, New Delhi, India; where for her performance and project work, she has been awarded the Essar Power Gold Medal, sponsored by Essar Power Ltd., and the University Topper Gold Model for Power Engineering batch 2019.
Her passion lies in using renewable energy resources with mechanical forces to reach net zero energy for residential buildings that have a positive impact on environment and people's life. As a driven professional, she is constantly seeking new challenges and opportunities to develop her skills and make meaningful contributions to the field of energy.
Research Interests[edit | edit source]
- Solar Photovoltaics
- Electrical Power System
- Optimization and Control
- Thermal Battery
- Heat pumps
Academic[edit | edit source]
Doctor of Philosophy (2022-26)
Electrical and Computer Engineering, Western University, ON, Canada
Courses:
Modeling Power System | Grade: 92/100
Energy Conversion | Grade: 80/100
Flexible AC Transmission System | Grade: 94/100
HVAC 1 | Grade: 88/100
HVAC 2 | Grade: Audit
Master of Technology (2020-22)
Electrical Power System and Management, Jamia Millia Islamia, New Delhi, India
SGPA: 9.62
Bachelor of Technology (2015-19)
Power Engineering, National Power Training Institute, New Delhi, India
SGPA: 8.75
PhD Research Proposal[edit | edit source]
Solar Photovoltaics integration with Heat pumps and Thermal Batteries: Proposing a Sustainable and Economical Model to Supply Thermal Loads and Electrical loads for Decarbonization of Residential Sectors in Canada.
The main aim of this research project is to provide the thermal and electrical loads of residential houses using green and clean PV technology in Canada. For this, a grid-tied net-metered PV system will be sized to provide electricity to run the HP (both air-air and air-water source) in aggregate over the whole year. In this variant, electricity will be available to the HP through the PV first and then the grid whenever PV electricity is not available. To reduce the grid dependency and increase the self-consumption rate of the PV system, a thermal battery would be utilized to store the excess thermal energy and used when required. To achieve the goal a comprehensive literature review will be conducted to highlight the importance of net zero energy goals and decarbonization of residential sectors. Then, the integration, validation, and simulation of the individual models will be done. Furthermore, these individual models will be integrated whose performances will be analyzed using key performance indicators and will be validated against the actual/real thermal+electrical load data of a residential house to obtain an optimal cost-effective, and sustainable system. The integrated systems will be then validated by the experimental setup of the system model on a residential house. The results of this experimental setup will be analyzed in the scope to determine the PV+HP+TB system advantages and feasibility against conventional fossil-fuel systems. This thesis will also focus on replacing the electric battery with a thermal battery in the retrofit system and analyzing the scope based on cost and efficiency.
Projects[edit | edit source]
PhD[edit | edit source]
- Residential Sizing of Solar Photovoltaic Systems and Heat Pumps for Net Zero Sustainable Thermal Building Energy
House Model
The house model represents a one-story residential building located in London, Ontario, Canada, and is designed to calculate the space heating and cooling loads required for optimal system performance. This model considers various factors contributing to energy losses, including the walls, ceiling, floor, people, lighting system, electrical equipment, air infiltration, doors, and windows. The heating load calculations involve determining the areas of walls, windows, doors, and floors exposed to the outside and calculating the overall heat transfer coefficients for these surfaces. Cooling load calculations use the Radiant Time Series Method (RTSM), which simplifies the process by considering sol-air temperature and various heat gains from windows, exterior surfaces, lighting, equipment, people, and infiltration. The model's accuracy was validated by comparing its results with those obtained using the Hourly Analysis Program (HAP), showing minor deviations and providing a conservative estimate. This detailed modeling ensures precise hourly and monthly thermal load profiles, crucial for optimizing the system's energy performance.
Black Box Heat Pump Model
The black box heat pump (BBHP) model was developed using the datasheet of Goodman Air-Air heat pumps and employs supervised regression learning to create mathematical functions for the coefficient of performance (COP) in both heating and cooling modes. In heating mode, the COP is modeled as a second-degree polynomial function of the ambient temperature, with polynomial curves fitted for various rated capacities using regression techniques. For cooling mode, the COP is modeled based on ambient temperature and indoor dry bulb and wet bulb temperatures, resulting in a more complex polynomial correlation. The model was validated against the manufacturer's data, showing an average deviation of approximately 2.5% for heating COP and 1.3% for cooling COP. This validation ensures the reliability of the BBHP model in predicting the heat pump's performance under different temperature conditions, crucial for accurate energy consumption calculations.
Solar PV Model
The solar photovoltaic (PV) model is designed to calculate the optimal size of a PV system to supply the thermal load for the residential house. The model uses a back-calculation method integrated with the System Advisor Model (SAM) in Python, allowing for precise estimation of the PV capacity required based on the hourly power consumption of the heat pump and local meteorological data. This approach considers total load demand, local weather data, and system losses to calculate the necessary PV system size. The model simulates the PV system performance using SAM, with parameters tailored to the specific location and system configuration. Performance metrics such as self-consumption and self-sufficiency are evaluated to assess the effectiveness of the PV system in minimizing energy exchange with the grid. The results indicate that using monthly load profiles significantly reduces the energy sent to/from the grid compared to detailed hourly simulations, thereby enhancing self-consumption and self-sufficiency. This PV model provides a robust framework for designing and evaluating PV systems for residential thermal energy applications.
Key findings:
1. Self-Consumption and Self-Sufficiency: The integration of solar PV systems with heat pumps significantly improves self-consumption and self-sufficiency rates. The study shows that monthly load profiles can reduce the energy exchanged with the grid by 43% compared to hourly simulations, increasing self-consumption and self-sufficiency from 30% to 60%. This highlights the importance of granular modeling to optimize system performance and reduce reliance on grid energy.
2. Performance Metrics: The study introduces performance metrics such as self-consumption and self-sufficiency to evaluate the effectiveness of the PV-heat pump integration. The designed PV system demonstrated a self-consumption rate of 30.02% and a self-sufficiency rate of 30.06%, indicating that a significant portion of the energy required by the heat pump can be met by the PV system over its lifetime.
- Geographical Dependence of Open Hardware Optimization: Case Study of Solar Photovoltaic Racking
Key findings:
1. Economic Variability: The study reveals significant geographical variability in the costs of wood and metal materials used for PV racking systems. Wood-based racks are more cost-effective in North America and some South American countries, while metal racks are cheaper in Central and South America.
2. Local Optimization: The research emphasizes the need for local optimization in open hardware designs to account for material availability and cost differences. This local adaptability can enhance the economic accessibility of solar PV systems.
3. Cost Comparison: Wood-based racks can reduce capital costs by 49% to 77% compared to proprietary racking systems in certain regions. However, the cost-effectiveness of wood versus metal depends heavily on local material prices and availability.
4. Technical Considerations: The study addresses technical aspects such as fire resistance, electrical grounding, and weathering for both wood and metal racks. Wood racks require additional grounding for metal-framed PV modules, while aluminum racks are more straightforward to ground but can be more expensive in some regions.
5. Sustainable Development: Open-source hardware, coupled with distributed manufacturing, can drive sustainable development by making solar PV systems more affordable and accessible. The study suggests that local manufacturing and do-it-together (DIT) methodologies can support community-based production and innovation.
M.Tech[edit | edit source]
- A Novel Control Strategy Based on Neural Networks for Improving Microgrid Operation
Microgrids based on solar PV and battery storage (BESS) are extensively employed for residential and commercial purposes. Single phase inverter is utilized to connect the microgrid to the main power grid.
Any microgrid's energy flow control strategy is critical for maximizing the utilization of energy storage technologies and renewable energy sources. The electricity flow control involving the microgrid and the utility grid is determined by the modes of operation of single-phase grid-linked inverters. Therefore, in this project, a Neural Network control based single phase grid linked inverter is proposed. The control technique used is based on a function-fitting Neural Network to provide an efficient power transfer between the microgrid and the main power grid. The bidirectional converter is used for controlling the discharging and charging operations of the battery. Microgrid voltage regulation is accomplished by single-phase inverters during grid integration mode and boost converters during isolation mode. The proposed methodology efficiently manages the power transfer between the main grid and microgrid as well as the voltage regulation of the DC bus.
- Energy Management of Grid Connected Renewable Energy Sources based Microgrid using Grey Wolf Optimization
As electricity demands grow, the current grid structure becomes unbalanced, resulting in a variety of issues such as load shedding and unbalanced voltage, all of which have an impact on end users. To escape such scenarios, the major alternative now is to satisfy demand through generation. However, the globe is already running out of conventional energy sources, so producing additional power isn't a viable option. The power industry has adopted microgrids as well as smart grids, which may make electric power systems more reliable and efficient by the use of information and communication technology. Renewable technology enhances the available energy resources. This knowledge additionally empowers the coordination of larger amounts of sustainable power sources and traditional energy sources. The inexhaustible sources are not dispatchable, the power yield is difficult to be controlled. The way the issues of sustainable power sources are tackled in the coming decades will have a significant impact on future energy management. In light of these facts, work has focused on constructing a microgrid for renewable energy management utilizing a genetic algorithm. The proposed study would use a grey wolf-based optimization method to learn in two steps. This evolutionary algorithm assigns the appropriate set of solar or windmill generators, or biomass generators, to meet a specific load requirement. In this study, an appropriate fitness function is applied, with the distance of the source plant being taken into account for power generation loss and reduction. The experiment was carried out on a real data set and the results reveal that the suggested work outperforms earlier approaches in terms of various performance parameters.
B.Tech[edit | edit source]
- Automatic Power Factor Detection and Correction
Automatic Power Factor Detection and Correction for Inductive Loads, with the goal of lowering residential power expenditures. This APFC system used capacitors to compensate for the reactive power drawn by inductive loads, improving the power factor and reducing energy consumption. The capacitors are connected to the load through a power factor controller, which monitors the load and switches the capacitors on or off as needed to maintain the desired power factor. The APFC system was designed to operate automatically using Arduino. This was an experimental project done to adjust the capacitors to maintain the desired power factor with the help of Arduino.
- Prajwal (Light)
E-waste is one of the major problems the world is facing these days. It is not only responsible for environmental problems but also has an indirect effect on our health and a direct impact on our food resources. Environment conservation has always been a priority at Enactus NPTI. This is what this project focuses on i.e., dealing with fused CFLs, which not only helps in recycling E-waste but also makes it sustainable for a long time and cost 77% less than normal CFL bulbs, thus giving its contribution in saving money as well as the environment. It is part of an entrepreneurial project having the perspective to see an opportunity and the talent to create value from that opportunity representing Enactus NPTI at the national level. The project also aimed to help people teach them to make LEDs with cheap materials.
